Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs
| Autor: | Root, Jonathan, Saligrama, Venkatesh, Qian, Jing |
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| Rok vydání: | 2016 |
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| Druh dokumentu: | Working Paper |
| Popis: | We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on $n$-point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly at $\alpha$-false alarm level if the predicted score is in the $\alpha$-percentile. The resulting anomaly detector is shown to be asymptotically optimal in that for any false alarm rate $\alpha$, its decision region converges to the $\alpha$-percentile minimum volume level set of the unknown underlying density. In addition, we test both the statistical performance and computational efficiency of our algorithm on a number of synthetic and real-data experiments. Our results demonstrate the superiority of our algorithm over existing $K$-NN based anomaly detection algorithms, with significant computational savings. Comment: arXiv admin note: substantial text overlap with arXiv:1502.01783, arXiv:1405.0530 |
| Databáze: | arXiv |
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